It should be noted that a list of the references referred to in this document may be found at the end of the document.
In the past few years, projector-camera systems have been used in many applications ranging from 3D reconstruction to multi-projector visualization. For many applications, the main task is to establish the correspondence between the pixels of a projector and those of a camera (see FIG. 1). This is accomplished by projecting known light patterns on an unknown scene viewed by a camera. The projector “applies” textures to the scene and the patterns are designed (or structured) such that it is possible to identify which projector pixel illuminates the part of the scene viewed by a camera pixel. Different patterns can be used: a survey by Salvi et al. classified these patterns in 3 categories, namely, direct codification, neighborhood codification and time-multiplexing. The Gray code is probably the best known time-multiplexing code[14]. It uses a succession of patterns composed of white and black stripes. For this reason, it is also known as a stripe-based code. The width of the stripes vary with the patterns.
When working with a structured light system, many sources of error can affect image formation. These sources of error can be classified as originating from projection, imaging or from the overall system:                Projection Depth of field and chromatic aberration affect the detectability of spatial transitions. Calibration parameters vary due to thermal instability. Error is introduced by dithering and signal synchronization.        Imaging Depth of field, anti-aliasing filter and small fill factor limit the detectability of the pattern. Also, when using color cameras, artifacts are introduced by Bayer color processing and the Bayer matrix itself.        Overall system Surface properties and ambient light impact the Signal-to-Noise Ratio (SNR). It is also reduced by high frame rates, or non-uniform sensitivity of the projection and of the camera.        
Previous work in this field has attempted to overcome these errors. A recent paper by Salvi et al. [26] contains an extensive survey of structured light (SL) techniques. Few methods use energy minimization frameworks for the decoding of patterns [5, 32, 16, 29, 17]. Some use these energy minimization frameworks to remove ambiguity in their code [5, 32, 16], while others use it to reduce sensitivity to noise [29, 17]. In [20], the best focus distance is used as a constraint in the self-calibration of a SL system.
It should be noted that, however, there is a large body of literature on the problem of thresholding, segmentation and deconvolution [28, 22]. Some methods simultaneously segment and deconvolve the images [24, 2]. However these methods do not address or take advantage of the aspects that are specific to a structured light system.
So far, no reference has been found which explicitly models blurring.
It is therefore an object of the present invention to mitigate if not overcome the shortcomings of the prior art.